{
 "cells": [
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {
    "id": "zPjMocS6JIUz"
   },
   "source": [
    "# Stacked Physics-Informed Neural Networks (Stacked PINNs) in Neuromancer\n",
    "\n",
    "This tutorial demonstrates the use of stacked (multi-fidelity) PINNs for solving challenging multiscale differential equations in the Neuromancer library.\n",
    "\n",
    "<img src=\"../figs/multifidelity_pinn.png\" width=\"1200\">  \n",
    "\n",
    "Despite the success of physics-informed neural networks, the \"vanilla\" formulation of PINNs [3] tend to fail to learn the solution of certain dynamical systems. In particular, in the presence of multiple fixed points and/or highly oscillatory, multiscale systems, the learned solution might converge to the trivial solution.\n",
    "\n",
    "In this notebook, we demonstrate how to solve these classes of problems using Stacked PINNs. This method consists of stacking multifidelity networks [1,2] in a composition manner: starting from a single-fidelity network (Step 0, c.f. figure above), the output is fed into a multifidelity block, consisting of two networks: a linear network (with no activation functions), and a nonlinear network. The nonlinear network takes as input the outputs of Step 0 plus the original inputs. The result of the multifidelity layer is a convex combination of the outputs of linear and nonlinear networks, weighted by a learnable parameter $\\alpha$ as shown below:\n",
    "\n",
    "<img src=\"../figs/multifidelity_detail.png\" width=\"600\">  \n",
    "\n",
    "In this architecture, the output of each of the stacking layers can be seen as a lower fidelity model for the next layer. This composition process progressively improves the learned solution.\n",
    "\n",
    "In the example below, we demonstrate the example of a damped pendulum, where the classical formulation of PINNs fail to converge to the correct solution, and we show how the stacked model allows us to solve the problem with higher accuracy.\n",
    "\n",
    "\n",
    "### References\n",
    "\n",
    "[1] [Howard, Amanda A., et al. (2023) Stacked networks improve physics-informed training: applications to neural networks and deep operator networks.](https://arxiv.org/abs/2311.06483)\n",
    "\n",
    "[2] [Heinlein, Alexander, et al. (2023) Multifidelity domain decomposition-based physics-informed neural networks for time-dependent problems.](https://arxiv.org/abs/2401.07888)\n",
    "\n",
    "[3] [Raissi, M., Perdikaris, P., & Karniadakis, G. E. (2017). Physics informed deep learning (part i): Data-driven solutions of nonlinear partial differential equations.](https://www.sciencedirect.com/science/article/abs/pii/S0021999118307125)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "Y61YA90-WIZ1"
   },
   "source": [
    "## Install (Colab only)\n",
    "Skip this step when running locally."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 1000
    },
    "id": "WZrPCr9GWEAJ",
    "outputId": "d0ff6dfe-de2a-4675-a36c-e2a7fce486d9"
   },
   "outputs": [],
   "source": [
    "# !pip install \"neuromancer[examples] @ git+https://github.com/pnnl/neuromancer.git@master\"\n",
    "# !pip install pyDOE"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Imports"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "import torch\n",
    "import torch.nn as nn\n",
    "import matplotlib.pyplot as plt\n",
    "from neuromancer.dataset import DictDataset\n",
    "from neuromancer.modules import blocks\n",
    "from neuromancer.system import Node\n",
    "from neuromancer.constraint import variable\n",
    "from neuromancer.loss import PenaltyLoss\n",
    "from neuromancer.problem import Problem\n",
    "from neuromancer.trainer import Trainer\n",
    "from neuromancer.dynamics.integrators import integrators\n",
    "import numpy as np\n",
    "\n",
    "# filter some user warnings from torch broadcast\n",
    "import warnings\n",
    "warnings.filterwarnings(\"ignore\")\n",
    "\n",
    "\n",
    "# Device configuration\n",
    "device = torch.device('cpu')\n",
    "torch.manual_seed(42);\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Problem Setup\n",
    "\n",
    "**The Pendulum Problem** is a fundamental example in classical mechanics, where the equations of motion describe the behavior of a simple pendulum under the influence of gravity and damping. The problem is given by the system of first-order ordinary differential equations:\n",
    "\n",
    "$$\n",
    "\\frac{ds_1}{dt} = s_2,\n",
    "$$\n",
    "$$\n",
    "\\frac{ds_2}{dt} = -\\frac{b}{m}s_2 - \\frac{g}{L}\\sin(s_1).\n",
    "$$\n",
    "\n",
    "Here, $s_1$ represents the angular displacement, and $s_2$ represents the angular velocity of the pendulum.\n",
    "\n",
    "**Initial Conditions:**\n",
    "\n",
    "The initial conditions are given by:\n",
    "$$ s_1(0) = 0.5, $$\n",
    "$$ s_2(0) = 0.5. $$\n",
    "\n",
    "**Parameters:**\n",
    "\n",
    "The parameters for this problem are:\n",
    "- Mass of the pendulum $ m = 1 $ kg,\n",
    "- Length of the pendulum $ L = 1 $ m,\n",
    "- Damping coefficient $ b = 0.05 $ kg/s,\n",
    "- Acceleration due to gravity $ g = 9.81 $ m/s²,\n",
    "- Total simulation time $ T = 20 $ s.\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Parameters\n",
    "m = 1.0    # Mass (kg)\n",
    "L = 1.0    # Length (m)\n",
    "b = 0.05   # Damping coefficient (kg/s)\n",
    "g = 9.81   # Acceleration due to gravity (m/s^2)\n",
    "T = 20.0   # Total simulation time (s)\n",
    "\n",
    "# Initial conditions\n",
    "s1_0 = 0.5\n",
    "s2_0 = 0.5\n",
    "\n",
    "# Number of collocation points\n",
    "N_cp = 500\n",
    "\n",
    "# Time discretization\n",
    "t = torch.linspace(0, T, N_cp)\n",
    "\n",
    "# Initial conditions\n",
    "ic_t = t[0].unsqueeze(0).unsqueeze(0)\n",
    "ic_s1 = torch.tensor([s1_0]).unsqueeze(0)\n",
    "ic_s2 = torch.tensor([s2_0]).unsqueeze(0)\n",
    "\n",
    "# Collocation points\n",
    "t_train_cp = t[1:].unsqueeze(1)\n",
    "\n",
    "# Combine initial condition and collocation points for training\n",
    "t_train = torch.cat((ic_t, t_train_cp)).float()\n",
    "s_train = torch.cat((torch.tensor([[s1_0, s2_0]]), torch.zeros((t_train_cp.shape[0], 2))))\n",
    "\n",
    "t_train.requires_grad_(True)\n",
    "\n",
    "# Create dataset\n",
    "train_data = DictDataset({'t': t_train}, name='train')\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Network architecture\n",
    "\n",
    "In this part, we build two networks: a vanilla PINN, and the stacked PINN.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Neural net to solve the ODE problem\n",
    "# Variables in: t; variables out: s (s1, s2)\n",
    "net_vanilla = blocks.MLP(insize=1, outsize=2, hsizes=[200]*3, nonlin=nn.SiLU)\n",
    "\n",
    "net_stacked = blocks.StackedMLP(insize=1, outsize=2, n_stacked_mf_layers= 4, h_sf_size=[100]*3, nonlin=nn.SiLU,\n",
    "                                 h_nonlinear_sizes=[50]*5, h_linear_sizes=[20,20],\n",
    "                                 verbose=True)\n",
    "\n",
    "# Symbolic wrapper of the neural net\n",
    "# Inputs: t\n",
    "# outputs: s (s1, s2)\n",
    "ode_net_vanilla = Node(net_vanilla, ['t'], ['s'], name='net_vanilla')\n",
    "ode_net_stacked = Node(net_stacked, ['t'], ['s'], name='net_stacked')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Symbolic Neuromancer variables\n",
    "s = variable('s')\n",
    "s1 = s[:, [0]]  # Angular displacement\n",
    "s2 = s[:, [1]]  # Angular velocity\n",
    "t = variable('t')  # Time\n",
    "\n",
    "# Define the ODE system\n",
    "ds1_dt = s2\n",
    "ds2_dt = -(b/m) * s2 - (g/L) * torch.sin(s1)\n",
    "\n",
    "# Define the PINN form\n",
    "f_pinn1 = s1.grad(t) - ds1_dt\n",
    "f_pinn2 = s2.grad(t) - ds2_dt\n",
    "f_pinn1_grad = f_pinn1.grad(t)\n",
    "f_pinn2_grad = f_pinn2.grad(t)\n",
    "\n",
    "# Scaling factors for better convergence\n",
    "scaling_ic_vanilla = 1.0\n",
    "scaling_cp_vanilla = 1.0\n",
    "scaling_ic_stacked = 1.0\n",
    "scaling_cp_stacked = 10.0\n",
    "\n",
    "# ODE collocation points loss (MSE)\n",
    "l_cp1_vanilla = scaling_cp_vanilla * (f_pinn1 == 0.)^2\n",
    "l_cp2_vanilla = scaling_cp_vanilla * (f_pinn2 == 0.)^2\n",
    "\n",
    "l_cp1_stacked = scaling_cp_stacked * (f_pinn1 == 0.)^2\n",
    "l_cp2_stacked = scaling_cp_stacked * (f_pinn2 == 0.)^2\n",
    "\n",
    "l_cp1_vanilla.update_name(\"l_cp1_vanilla\")\n",
    "l_cp2_vanilla.update_name(\"l_cp2_vanilla\")\n",
    "l_cp1_stacked.update_name(\"c_cp1_stacked\")\n",
    "l_cp2_stacked.update_name(\"c_cp2_stacked\")\n",
    "\n",
    "\n",
    "# Initial condition loss (MSE)\n",
    "l_ic1_vanilla = scaling_ic_vanilla * (s1[0] == ic_s1 )^2\n",
    "l_ic2_vanilla = scaling_ic_vanilla * (s2[0] == ic_s2 )^2\n",
    "l_ic1_stacked = scaling_ic_stacked * (s1[0] == ic_s1 )^2\n",
    "l_ic2_stacked = scaling_ic_stacked * (s2[0] == ic_s2 )^2\n",
    "\n",
    "# Combined loss\n",
    "loss_vanilla = PenaltyLoss(objectives=[l_cp1_vanilla, l_cp2_vanilla, l_ic1_vanilla, l_ic2_vanilla], constraints=[])\n",
    "loss_stacked = PenaltyLoss(objectives=[l_cp1_stacked, l_cp2_stacked, l_ic1_stacked, l_ic2_stacked], constraints=[])\n",
    "\n",
    "# Construct the PINN optimization problem\n",
    "problem_vanilla = Problem(nodes=[ode_net_vanilla], loss=loss_vanilla, grad_inference=True)\n",
    "problem_stacked = Problem(nodes=[ode_net_stacked], loss=loss_stacked, grad_inference=True)\n",
    "\n",
    "# Optimizer\n",
    "initial_lr = 1e-3\n",
    "optimizer_vanilla = torch.optim.AdamW(problem_vanilla.parameters(), lr=initial_lr)\n",
    "optimizer_stacked = torch.optim.AdamW(problem_stacked.parameters(), lr=initial_lr)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " Total number of parameters in single-fidelity net= 81202\n",
      " Total number of parameters in multi-fidelity net= 64702\n"
     ]
    }
   ],
   "source": [
    "# Print parameter counts\n",
    "print(f\" Total number of parameters in single-fidelity net= {sum(p.numel() for p in net_vanilla.parameters())}\")\n",
    "print(f\" Total number of parameters in multi-fidelity net= {sum(p.numel() for p in net_stacked.parameters())}\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "MLP(\n",
      "  (nonlin): ModuleList(\n",
      "    (0-2): 3 x SiLU()\n",
      "    (3): Identity()\n",
      "  )\n",
      "  (linear): ModuleList(\n",
      "    (0): Linear(\n",
      "      (linear): Linear(in_features=1, out_features=200, bias=True)\n",
      "    )\n",
      "    (1-2): 2 x Linear(\n",
      "      (linear): Linear(in_features=200, out_features=200, bias=True)\n",
      "    )\n",
      "    (3): Linear(\n",
      "      (linear): Linear(in_features=200, out_features=2, bias=True)\n",
      "    )\n",
      "  )\n",
      ")\n"
     ]
    }
   ],
   "source": [
    "# Print vanilla net\n",
    "print(net_vanilla)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "StackedMLP(\n",
      "  (alpha): ParameterList(\n",
      "      (0): Parameter containing: [torch.float32 of size ]\n",
      "      (1): Parameter containing: [torch.float32 of size ]\n",
      "      (2): Parameter containing: [torch.float32 of size ]\n",
      "      (3): Parameter containing: [torch.float32 of size ]\n",
      "  )\n",
      "  (first_layer): MLP(\n",
      "    (nonlin): ModuleList(\n",
      "      (0-2): 3 x SiLU()\n",
      "      (3): Identity()\n",
      "    )\n",
      "    (linear): ModuleList(\n",
      "      (0): Linear(\n",
      "        (linear): Linear(\n",
      "          (linear): Linear(in_features=1, out_features=100, bias=True)\n",
      "        )\n",
      "      )\n",
      "      (1-2): 2 x Linear(\n",
      "        (linear): Linear(\n",
      "          (linear): Linear(in_features=100, out_features=100, bias=True)\n",
      "        )\n",
      "      )\n",
      "      (3): Linear(\n",
      "        (linear): Linear(\n",
      "          (linear): Linear(in_features=100, out_features=2, bias=True)\n",
      "        )\n",
      "      )\n",
      "    )\n",
      "  )\n",
      "  (layers): ModuleList(\n",
      "    (0-3): 4 x ModuleDict(\n",
      "      (linear): MLP(\n",
      "        (nonlin): ModuleList(\n",
      "          (0-2): 3 x Identity()\n",
      "        )\n",
      "        (linear): ModuleList(\n",
      "          (0): Linear(\n",
      "            (linear): Linear(\n",
      "              (linear): Linear(in_features=2, out_features=20, bias=True)\n",
      "            )\n",
      "          )\n",
      "          (1): Linear(\n",
      "            (linear): Linear(\n",
      "              (linear): Linear(in_features=20, out_features=20, bias=True)\n",
      "            )\n",
      "          )\n",
      "          (2): Linear(\n",
      "            (linear): Linear(\n",
      "              (linear): Linear(in_features=20, out_features=2, bias=True)\n",
      "            )\n",
      "          )\n",
      "        )\n",
      "      )\n",
      "      (nonlinear): MLP(\n",
      "        (nonlin): ModuleList(\n",
      "          (0-4): 5 x SiLU()\n",
      "          (5): Identity()\n",
      "        )\n",
      "        (linear): ModuleList(\n",
      "          (0): Linear(\n",
      "            (linear): Linear(\n",
      "              (linear): Linear(in_features=3, out_features=50, bias=True)\n",
      "            )\n",
      "          )\n",
      "          (1-4): 4 x Linear(\n",
      "            (linear): Linear(\n",
      "              (linear): Linear(in_features=50, out_features=50, bias=True)\n",
      "            )\n",
      "          )\n",
      "          (5): Linear(\n",
      "            (linear): Linear(\n",
      "              (linear): Linear(in_features=50, out_features=2, bias=True)\n",
      "            )\n",
      "          )\n",
      "        )\n",
      "      )\n",
      "    )\n",
      "  )\n",
      ")\n"
     ]
    }
   ],
   "source": [
    "# Print stacked net\n",
    "print(net_stacked)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Train networks"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Train \"vanilla\" net"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "epoch: 0  train_loss: 18.41791343688965\n",
      "epoch: 5000  train_loss: 0.006505195051431656\n",
      "epoch: 10000  train_loss: 0.00511967483907938\n",
      "epoch: 15000  train_loss: 0.004092948045581579\n",
      "epoch: 20000  train_loss: 0.004740165080875158\n",
      "epoch: 25000  train_loss: 0.003175815800204873\n",
      "epoch: 30000  train_loss: 0.0030456329695880413\n",
      "epoch: 35000  train_loss: 0.002559319604188204\n",
      "epoch: 40000  train_loss: 0.0025621061213314533\n",
      "epoch: 45000  train_loss: 0.0023774011060595512\n",
      "epoch: 50000  train_loss: 0.002386349719017744\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<All keys matched successfully>"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Number of epochs\n",
    "epochs_vanilla = 50001\n",
    "\n",
    "# Neuromancer trainer\n",
    "trainer_vanilla = Trainer(\n",
    "    problem_vanilla.to(device),\n",
    "    torch.utils.data.DataLoader(train_data, batch_size=t_train.shape[0],\n",
    "                                collate_fn=train_data.collate_fn, shuffle=False),\n",
    "    optimizer=optimizer_vanilla,\n",
    "    epochs=epochs_vanilla,\n",
    "    epoch_verbose=5000,\n",
    "    train_metric='train_loss',\n",
    "    dev_metric='train_loss',\n",
    "    eval_metric=\"train_loss\",\n",
    "    warmup=epochs_vanilla,\n",
    "    device=device\n",
    ")\n",
    "\n",
    "# Train PINN\n",
    "best_model_vanilla = trainer_vanilla.train()\n",
    "\n",
    "# Load best trained model\n",
    "problem_vanilla.load_state_dict(best_model_vanilla)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Train stacked net"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "epoch: 0  train_loss: 25.321632385253906\n",
      "epoch: 5000  train_loss: 0.0363730862736702\n",
      "epoch: 10000  train_loss: 0.02877199277281761\n",
      "epoch: 15000  train_loss: 0.023000376299023628\n",
      "epoch: 20000  train_loss: 0.02121264487504959\n",
      "epoch: 25000  train_loss: 0.01926872879266739\n",
      "epoch: 30000  train_loss: 0.018717575818300247\n",
      "epoch: 35000  train_loss: 0.017837664112448692\n",
      "epoch: 40000  train_loss: 0.016061697155237198\n",
      "epoch: 45000  train_loss: 0.01489781029522419\n",
      "epoch: 50000  train_loss: 0.014973273500800133\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<All keys matched successfully>"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Number of epochs\n",
    "epochs_stacked = 50001\n",
    "\n",
    "# Neuromancer trainer \n",
    "trainer_stacked = Trainer(\n",
    "    problem_stacked.to(device),\n",
    "    torch.utils.data.DataLoader(train_data, batch_size=t_train.shape[0],\n",
    "                                collate_fn=train_data.collate_fn, shuffle=False),\n",
    "    optimizer=optimizer_stacked,\n",
    "    epochs=epochs_stacked,\n",
    "    epoch_verbose=5000,\n",
    "    train_metric='train_loss',\n",
    "    dev_metric='train_loss',\n",
    "    eval_metric=\"train_loss\",\n",
    "    warmup=epochs_stacked,\n",
    "    device=device,\n",
    "    multi_fidelity=True\n",
    ")\n",
    "\n",
    "# Train PINN\n",
    "best_model_stacked = trainer_stacked.train()\n",
    "\n",
    "# Load best trained model\n",
    "problem_stacked.load_state_dict(best_model_stacked)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x600 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Visualize results\n",
    "\n",
    "# Single-fidelity predictions\n",
    "s_pred_vanilla = ode_net_vanilla(train_data.datadict)['s'].detach().cpu().numpy()\n",
    "s1_pred_vanilla = s_pred_vanilla[:, 0]\n",
    "s2_pred_vanilla = s_pred_vanilla[:, 1]\n",
    "t_train = train_data.datadict['t'].detach().cpu().numpy()\n",
    "\n",
    "# Multi-fidelity predictions\n",
    "s_pred_stacked = ode_net_stacked(train_data.datadict)['s'].detach().cpu().numpy()\n",
    "s1_pred_stacked = s_pred_stacked[:, 0]\n",
    "s2_pred_stacked = s_pred_stacked[:, 1]\n",
    "\n",
    "# Solve the ODE numerically in Neuromancer using 4th order Runge-Kutta\n",
    "class PendulumODE(nn.Module):\n",
    "    def __init__(self, b, m, g, L, insize=2, outsize=2):\n",
    "        super().__init__()\n",
    "        self.b = b\n",
    "        self.m = m\n",
    "        self.g = g\n",
    "        self.L = L\n",
    "        self.in_features = insize\n",
    "        self.out_features = outsize\n",
    "\n",
    "    def forward(self, x, t=None):\n",
    "        s1 = x[:, [0]]\n",
    "        s2 = x[:, [1]]\n",
    "        ds1_dt = s2\n",
    "        ds2_dt = -self.b / self.m * s2 - self.g / self.L * torch.sin(s1)\n",
    "        return torch.cat([ds1_dt, ds2_dt], dim=-1)\n",
    "\n",
    "pendulum_ode = PendulumODE(b, m, g, L)\n",
    "fxRK4 = integrators['RK4'](pendulum_ode, h=T/N_cp)\n",
    "y0 = torch.tensor([[s1_0, s2_0]])\n",
    "t_eval = torch.linspace(0, T, N_cp)\n",
    "\n",
    "# Solve the ODE numerically using RK4 integrator\n",
    "y_t = y0\n",
    "sol = [y_t]\n",
    "for t in t_eval[1:]:\n",
    "    y_t = fxRK4(y_t)\n",
    "    sol.append(y_t)\n",
    "sol = torch.stack(sol).detach().numpy()\n",
    "\n",
    "\n",
    "# Plot the results\n",
    "plt.figure(figsize=(12, 6))\n",
    "\n",
    "# Angular Displacement\n",
    "plt.subplot(2, 1, 1)\n",
    "plt.plot(t_train, s1_pred_vanilla, label='Predicted $s_1(t)$ (PINN)')\n",
    "plt.plot(t_train, s1_pred_stacked, label='Predicted $s_1(t)$ (Stacked PINN)')\n",
    "plt.plot(t_eval, sol[:,0,0], label='Numerical $s_1(t)$', linestyle='dashed')\n",
    "plt.xlabel('Time [s]')\n",
    "plt.ylabel('Angular Displacement [rad]')\n",
    "plt.legend()\n",
    "plt.grid(True)\n",
    "\n",
    "# Angular Velocity\n",
    "plt.subplot(2, 1, 2)\n",
    "plt.plot(t_train, s2_pred_vanilla, label='Predicted $s_2(t)$ (PINN)')\n",
    "plt.plot(t_train, s2_pred_stacked, label='Predicted $s_2(t)$ (Stacked PINN)')\n",
    "plt.plot(t_eval, sol[:,0,1], label='Numerical $s_2(t)$', linestyle='dashed')\n",
    "plt.xlabel('Time [s]')\n",
    "plt.ylabel('Angular Velocity [rad/s]')\n",
    "plt.legend()\n",
    "plt.grid(True)\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "alpha_0 = Parameter containing:\n",
      "tensor(0.0002, requires_grad=True)\n",
      "alpha_1 = Parameter containing:\n",
      "tensor(0.0408, requires_grad=True)\n",
      "alpha_2 = Parameter containing:\n",
      "tensor(0.0550, requires_grad=True)\n",
      "alpha_3 = Parameter containing:\n",
      "tensor(0.1466, requires_grad=True)\n"
     ]
    }
   ],
   "source": [
    "# Verify that final parameter alpha is small (see Eq. 11 of Howard, Amanda A. et al. 2023).\n",
    "for idx,alpha in enumerate(net_stacked.alpha):\n",
    "    print(f\"alpha_{idx} = {alpha}\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "accelerator": "GPU",
  "colab": {
   "collapsed_sections": [
    "Y61YA90-WIZ1",
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